DESCRIPTION (Verbatim from applicant's abstract): We propose to study temporal coding in the circuit to the brisk-sustained ganglion cell that is a crucial link in the visual system's reliability. Performance of the ganglion cell is limited by noise in the visual input and from several sources in the presynaptic circuit, but how the ganglion cell integrates a noisy signal for transmission to the brain is unknown. The standard theory for synaptic integration has been a passive dendritic tree coupled to an integrate-and-fire generator to code the signal as a spike rate. Two new facts imply a different theory: (1) dendrites of the ganglion cell express sodium channels, which amplify synaptic noise and are an integral part of the spike generator setting its spike frequency gain, and (2) the spike generator emphasizes high frequencies and responds to transient stimuli with high temporal precision, consistent with a "timing code" which has a greater efficiency than a rate code. We hypothesize that these observations are interrelated because enhancement of the high frequencies present in the noise of synaptic input may improve the spike generator's temporal precision. We propose to test this hypothesis by applying an "ideal observer" to the responses of a real cell and a model. The ideal observer is a computer program that discriminates between two stimuli using all information in a neural system's response. It calculates how reliably contrasts can be discriminated using a likelihood rule, and is therefore an appropriate method to compare fundamental performance of real cell, model, and human behavior. With data sets recorded from ganglion cells in an intact retina preparation and an existing computational model that includes stochastic voltage-gated channels and synaptic release, we propose to determine how the ganglion cell codes synaptic inputs and what factors limit its precision. We will compare the performance of real cell and model to determine what temporal features are coded most reliably, and we will determine how these features are coded by modifying features (e.g., channel kinetics, spike adaptation, slope and threshold of spike frequency vs. input curve) of the computational model. We will evaluate the contribution of voltage-gated channels to amplification of dendritic PSPs, and what spatial and temporal patterns enhance spike precision. Finally, we will determine the function of excitatory and inhibitory mPSPs, due to correlations among and between them. The project will help understand how neural circuits contribute to a precise temporal code in human vision.